翻译随着深度学习的飞速发展从文本到图像生成的研究也取得了重大进步。本课题尝试采用改进的生成对抗网络GAN将文本数据转换为写实性图像。生成对抗网络是2014年由Ian Goodfellow等人提出的一种深度学习架构用于在逼真的图像、音频、影像和文本等数据领域中生成新数据。GAN的结构包含两部分：一个生成器和一个鉴别器。生成器尝试生成尽可能逼真的新数据样本而鉴别器则评估生成器生成的数据样本与真实数据样

With the rapid development of deep learning, significant progress has been made in the field of generating realistic images from text data. This project attempts to use an improved Generative Adversarial Network (GAN) to convert text data into realistic images. GAN is a deep learning architecture proposed by Ian Goodfellow et al. in 2014, used to generate new data in the domains of realistic images, audio, video, and text. The GAN structure consists of two parts: a generator and a discriminator. The generator tries to generate new data samples that are as realistic as possible, while the discriminator evaluates the difference between the generated data samples and the real data samples, determining whether they are similar. These two parts are trained simultaneously, continually adjusting each other's model parameters, so that the generator continually generates more realistic data samples, while the discriminator continuously improves its ability to distinguish between real and fake data. This process can be described as a zero-sum game of adversarial process as the goals of the generator and discriminator are in direct conflict. GANs have achieved significant results in the fields of image generation, image transformation, speech transformation, text generation, and have wide-ranging application prospects.

In this article, I introduce perceptual loss and color difference loss into the traditional GAN framework, aiming to increase the similarity between the generated image and the real image, and reduce the color difference between them, allowing the generator to learn more details and color information. In addition, the depth and width of the generator network have been increased to better learn the color and texture of objects. Experimental results show that the method used in this article generates images with more accurate colors